[recommendation] - add manual tracing instrumentation

src/recommendationservice/recommendation_server.py

@@ -37,21 +37,14 @@
 tracer_provider = TracerProvider()
 trace.set_tracer_provider(tracer_provider)
 tracer_provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter()))
+tracer = trace.get_tracer("recommendationservice")
 
 
 class RecommendationService(demo_pb2_grpc.RecommendationServiceServicer):
     def ListRecommendations(self, request, context):
-        max_responses = 5
-        # fetch list of products from product catalog stub
-        cat_response = product_catalog_stub.ListProducts(demo_pb2.Empty())
-        product_ids = [x.id for x in cat_response.products]
-        filtered_products = list(set(product_ids)-set(request.product_ids))
-        num_products = len(filtered_products)
-        num_return = min(max_responses, num_products)
-        # sample list of indicies to return
-        indices = random.sample(range(num_products), num_return)
-        # fetch product ids from indices
-        prod_list = [filtered_products[i] for i in indices]
+        prod_list = get_product_list(request.product_ids)
+        span = trace.get_current_span()
+        span.set_attribute("app.products_recommended.count", len(prod_list))
         logger.info("[Recv ListRecommendations] product_ids={}".format(prod_list))
         # build and return response
         response = demo_pb2.ListRecommendationsResponse()
@@ -66,12 +59,34 @@ def Watch(self, request, context):
         return health_pb2.HealthCheckResponse(
             status=health_pb2.HealthCheckResponse.UNIMPLEMENTED)
 
+
+def get_product_list(request_product_ids):
+    with tracer.start_as_current_span("get_product_list") as span:
+        max_responses = 5
+        # fetch list of products from product catalog stub
+        cat_response = product_catalog_stub.ListProducts(demo_pb2.Empty())
+        product_ids = [x.id for x in cat_response.products]
+        span.set_attribute("app.products.count", len(product_ids))
+
+        filtered_products = list(set(product_ids) - set(request_product_ids))
+        num_products = len(filtered_products)
+        span.set_attribute("app.filtered_products.count", num_products)
+
+        num_return = min(max_responses, num_products)
+        # sample list of indicies to return
+        indices = random.sample(range(num_products), num_return)
+        # fetch product ids from indices
+        prod_list = [filtered_products[i] for i in indices]
+        return prod_list
+
+
 def must_map_env(key: str):
     value = os.environ.get(key)
     if value is None:
         raise Exception(f'{key} environment variable must be set')
     return value
 
+
 if __name__ == "__main__":
     logger.info("initializing recommendationservice")